Quantitative research
How to Evaluate a Market Regime Detector Without Fooling Yourself
A detector can look fast simply because it changes its mind constantly. This held-out synthetic benchmark shows why regime research needs ground truth, causal features, strong baselines, and a scorecard that keeps latency beside stability.
How Spread Foundry Uses Regime Context
FerroWave turns trailing market history into a versioned evidence pack. Spread Foundry uses that context to explain suitability and risk without treating a regime label as a forecast or hiding structurally valid spreads.
Advanced Signal Decompositions Need the Right Test
SWT, synchrosqueezing, EMD, VMD, and EWT encode different assumptions. Four synthetic diagnostics show what each method earns, where a direct baseline still wins, and why one leaderboard cannot answer every regime question.
Options Spread Analytics Should Be a Workflow, Not a Table
Spread analytics becomes useful when it moves from isolated metrics to a guided workflow: market posture, symbol context, strategy fit, candidate ranking, and risk review.
Wavelet-Domain Regime Detection with the MODWT
Markets change character at different speeds. The MODWT splits a return series into scale components whose variances add up exactly — which turns a vague question about regimes into an estimable one.